Is sleep in animals affected by prior waking experiences?
Bibliographic record
Abstract
Abstract Methods to assess changes in the mental state of animals in response to their environment can be used to provide information to enhance animal welfare. One of the most profound changes of mental state observable in mammals is the change between wakefulness and sleep. Sleeping mammals have characteristics that are similar to one another and are measurable, such as specific behaviours, changes in responsiveness to external stimuli and changes in electrophysiology and neurochemistry. Although sleep is a ubiquitous behaviour in the life of mammals, there has been relatively little research on this topic in domesticated animals. All animals are motivated to sleep and this motivation increases after a prolonged period of wakefulness. In humans, sleep can be affected by what has occurred in the prior period of wakefulness and this has also been demonstrated in some non-human mammals. An important aspect of human sleep medicine is the association between stress and subsequent sleep disturbances. Studying changes in amount, bout length, distribution or type of sleep after exposure to potentially stressful events, could help us understand how animals respond to changes in their environment. It is possible that different types of stressors could affect sleep characteristics in different ways and that monitoring and identifying these changes could be useful in providing an additional way of identifying management procedures that have the potential to affect welfare. Sleep measurement is a potentially valuable tool in studies to assess animal welfare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".